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10.46243/jst.2021.v6.i04.pp377-382 registered

Sentiment Analysis using Machine Learning (The Sorting Hat)

Resolves to https://www.jst.org.in/index.php/pub/article/view/772

Held by Longman Publishers (India) · prefix 10.46243 live · DOI address https://doi.org/10.46243/jst.2021.v6.i04.pp377-382

Registered 29 Sep 2026 via crossref · record version 2 · last change 30 Sep 2026, 12:00 AM · record sha256 9d4b1a2666842dc9…

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What the DOI identifies

JournalArticle — an article in a journal · Digital · Visual · en

Sentiment Analysis using Machine Learning (The Sorting Hat) (PrincipalTitle)

Published 2021-08-16

Part of Journal of Science & Technology · ISSN 2456-5660 · volume 06 · issue 01 · pages 377–382

Agents

  • Prasad Wagh (author)
  • Pratik Jaiswal (author)
  • Ankit Rahangdale (author)
  • Longman Publishers (publisher)

Identifiers DOI 10.46243/jst.2021.v6.i04.pp377-382

Abstract

Sarcasm and Hate Speech is impacting societal harmony and peace. Considering the magnitude of this harmonious impact, there is a need to find a solution to curb the online spread of Hate Speech and Sarcasm. Detection of hate speech and sarcasm is being tackled with various approaches like manual checks, deep learning techniques in recent times, and statistical-based classification algorithms. These methods are unreliable due to the non-binary(true or false) nature of the tweets. Categorizing tweets requires deeper investigation such as classification on entirely positive or entirely negative rather than binary classification. In this paper - a snippet - The Sorting Hat, to detect sarcasm, hate-speech, and sentiments in the tweets using SVM (Support Vector Machine) and LSTM (Long short term memory) is proposed. The Sorting Hat classifies a given tweet into one of the six degrees of classification - “Positive”, “Negative”, “Neutral”, “Sarcasm”, “Non-sarcasm”, “Hate-speech”. The basic meaning of sarcasm which comes into our mind is a positive statement or sentiment attached to a negative situation or vice versa.The current system works on the outside whiсh has been assigned tо а раrtiсulаr tорiс. Current systems also do not determine the imрасt rating, the results are limited to whether they can be included in the раrtiсulаr processing field and do not allow retrieval of data based on user-generated query meaning that it has been selected.. Whereas the Sorting Hat will collect the tweets from the users manually. Collected tweets will be considered for further processing. We will then аррly the suрervised аlgоrithm оn the stоred dаtа. The supervised algorithm used in the оur system is Suрроrt Veсtоr Mасhine (SVM). The results of the algorithms i.e. emotions will be represented in a graphical way (bar charts). The proposed system works better compared to the existing one. This is because we will be able to obtain calculated figures from reрresentаtiоn оf result саn hаvе аny imрасt in the field of а раrtiсulаr. The overall product experience using The Sorting Hat largely intervenes the impulsive behavior of posting tweets, and thereby provides the solution to curb rampant spread of Hate Speech and better understanding of sarcastic tweets.

Licence https://creativecommons.org/licenses/by/4.0/

System metadata — ISO 26324:2025, Annex B · DOI Handbook 10.1

Each element by the standard's name (Annex B: reference elements, then administrative) and the Handbook's (in grey), read off the record above.

ElementValueIn the record
DOI Name
DOI name
10.46243/jst.2021.v6.i04.pp377-382doi
Referent Type
referentType
Creationreferent
Referent Sub-Type
referentSubType
JournalArticle — an article in a journaltype
Referent Name(s)
referentName(s)
Sentiment Analysis using Machine Learning (The Sorting Hat) (PrincipalTitle, en)titles
Basic Metadata
basicMetadata
author: Prasad Wagh
author: Pratik Jaiswal
author: Ankit Rahangdale
publisher: Longman Publishers
published: 2021-08-16
part of: Journal of Science & Technology · ISSN 2456-5660 · vol. 06 · no. 01 · pp. 377–382
language: en
form: Digital · Visual · Language
agents, dates, container, language, structural_type, modes, characters
Referent Identifier(s)
alternateIdentifier(s)
none besides the DOIidentifiers, relations (IsSameAs)
Registration Authority
registrationAuthorityCode
Crossref — issued by Crossref (member 25296); held here as a copyrecord.source_agency (our code, ra_doi_name, for names issued here once appointed)
Created Date
issueDate
2026-09-11record.registered (when the DOI name was first registered)
relatedIdentifiersnone needed — the descriptive metadata is in this recordcontainer, relations (only where the descriptive metadata lives at another identifier)

complete Every System Metadata element is here, with the basic metadata a journal article needs.

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#WhenWhatByChanges
129 Sep 2026, 10:00 PMregister
registered at Crossref; record read from api.crossref.org
Administrator (admin) 76 fields set · sha256 28264ca7a481…
230 Sep 2026, 12:00 AMupdate
record re-read from api.crossref.org
Administrator (admin)
container.titles.0.value: Journal of Science & Technology → Journal of Science & Technology

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